This study investigated the osteogenic effect of low-magnitude high-frequency vibration (LMHFV, 35 Hz, 0.3g) on the enhancement of fracture healing in rats with closed femoral shaft fracture by comparing with sham-treated control. Assessments with plain radiography, Caspase inhibitor micro-CT as well as histomorphometry showed that the amount of callus was significantly larger (p = 0.001 for callus area, 2 weeks posttreatment); the remodeling

of the callus into mature bone was significantly faster (p = 0.039, 4 weeks posttreatment) in the treatment group. The mechanical strength of the healed fracture in the treatment group at 4 weeks was significantly greater (p < 0.001). The results showed the acceleration of callus formation, mineralization, and fracture healing in the treatment group. It is concluded that LMHFV enhances healing in the closed femoral shaft fracture in rats. The potential clinical advantages shall

be confirmed in the subsequent clinical trials. (C) 2008 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 27:458-465, 2009″
“In epilepsy diagnosis or epileptic seizure detection, much effort has been focused on finding effective combination of feature extraction and classification methods. In this paper, we develop a wavelet-based sparse functional linear model for representation of EEG signals. LY2606368 solubility dmso The aim of this modeling approach is to capture discriminative random components check details of EEG signals using wavelet variances. To achieve this goal, a forward search algorithm is proposed for determination of an appropriate wavelet decomposition level. Two EEG databases from University of Bonn and University of Freiburg are used for illustration of applicability of the proposed method to both epilepsy diagnosis

and epileptic seizure detection problems. For this data considered, we show that wavelet-based sparse functional linear model with a simple classifier such as 1-NN classification method leads to higher classification results than those obtained using other complicated methods such as support vector machine. This approach produces a 100 % classification accuracy for various classification tasks using the EEG database from University of Bonn, and outperforms many other state-of-the-art techniques. The proposed classification scheme leads to 99 % overall classification accuracy for the EEG data from University of Freiburg.”
“Myelodysplastic syndromes are a heterogeneous group of diseases characterized by ineffective hematopoiesis and the propensity to leukemic transformation. Their pathogenesis is complex and likely depends on interplay between aberrant hematopoietic cells and their microenvironment.